"""
Bio-Agronomist Agent: Climate & Agronomic Intelligence Tool
Fetches Advanced Agricultural Metrics from Open-Meteo Historic & Forecast APIs.

Provides massive toolset upgrades over standard weather:
1. Growing Degree Days (GDD): Crucial for crop maturity cycle calculations.
2. Evapotranspiration (ET₀): Tells the exact crop water requirement.
3. Soil Moisture (0-100cm): Tracks drought stress across differing root zones.
4. Solar Radiation (GHI/DNI): Key for photosynthetic rate/yield potential.
"""

import requests
import json
import logging
from datetime import datetime, timedelta

# Configure Logger
logger = logging.getLogger("BioAgronomist.ClimateTool")
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
logger.addHandler(handler)

class AgroClimateIntelligenceTool:
    def __init__(self):
        self.ARCHIVE_URL = "https://archive-api.open-meteo.com/v1/archive"
        self.FORECAST_URL = "https://api.open-meteo.com/v1/forecast"
        
    def fetch_annual_agronomic_baseline(self, lat: float, lon: float) -> dict:
        """
        Fetches a 1-year historical baseline of advanced agronomic variables.
        Used to determine base viability for crop selection.
        """
        end_date = datetime.now() - timedelta(days=7)  # Archive API has ~5 day lag
        start_date = end_date - timedelta(days=365)
        
        logger.info(f"Fetching 1-Year Agronomic Baseline for {lat}, {lon} ({start_date.strftime('%Y-%m-%d')} to {end_date.strftime('%Y-%m-%d')})")
        
        params = {
            "latitude": lat,
            "longitude": lon,
            "start_date": start_date.strftime("%Y-%m-%d"),
            "end_date": end_date.strftime("%Y-%m-%d"),
            "daily": [
                "temperature_2m_max", 
                "temperature_2m_min", 
                "precipitation_sum", 
                "et0_fao_evapotranspiration", # Crop Water Demand
                "shortwave_radiation_sum",    # Photosynthetic potential
            ],
            "timezone": "auto"
        }
        
        try:
            resp = requests.get(self.ARCHIVE_URL, params=params, timeout=30)
            if resp.status_code != 200:
                logger.error(f"Archive API failed: HTTP {resp.status_code}")
                return {"error": "API Failure"}
                
            data = resp.json().get("daily", {})
            times = data.get("time", [])
            p_sum = data.get("precipitation_sum", [])
            t_max = data.get("temperature_2m_max", [])
            et0   = data.get("et0_fao_evapotranspiration", [])
            rad   = data.get("shortwave_radiation_sum", [])
            
            if not times:
                return {"error": "Empty data array"}

            # Aggregate into Monthly Summaries
            monthly_data = {}
            for i in range(len(times)):
                month_key = times[i][:7] # YYYY-MM
                if month_key not in monthly_data:
                    monthly_data[month_key] = {"rain": [], "tmax": [], "et0": [], "rad": []}
                
                if p_sum[i] is not None: monthly_data[month_key]["rain"].append(p_sum[i])
                if t_max[i] is not None: monthly_data[month_key]["tmax"].append(t_max[i])
                if et0[i] is not None:   monthly_data[month_key]["et0"].append(et0[i])
                if rad[i] is not None:   monthly_data[month_key]["rad"].append(rad[i])

            summary = []
            for m, vals in sorted(monthly_data.items()):
                summary.append({
                    "month": m,
                    "avg_temp_max_c": round(sum(vals["tmax"])/len(vals["tmax"]), 1) if vals["tmax"] else 0,
                    "total_rain_mm": round(sum(vals["rain"]), 1) if vals["rain"] else 0,
                    "total_et0_mm": round(sum(vals["et0"]), 1) if vals["et0"] else 0,
                    "avg_daily_radiation_mj_m2": round(sum(vals["rad"])/len(vals["rad"]), 2) if vals["rad"] else 0,
                    # Hydrological Stress: if Evapotranspiration > Rain, crop requires irrigation
                    "irrigation_deficit_mm": round((sum(vals["et0"]) or 0) - (sum(vals["rain"]) or 0), 1)
                })
            
            logger.info("Successfully compiled 12-month agronomic baseline.")
            return {"monthly_agronomics": summary, "annual_analysis_ready": True}
            
        except Exception as e:
            logger.error(f"Exception during agronomic baseline fetch: {e}")
            return {"error": str(e)}

    def fetch_current_soil_moisture_stress(self, lat: float, lon: float) -> dict:
        """
        Fetches LIVE soil moisture levels to evaluate immediate planting conditions.
        Crucial for deciding if sowing can happen now without irrigation.
        """
        logger.info(f"Fetching Live Soil Moisture Profile for {lat}, {lon}")
        
        params = {
            "latitude": lat,
            "longitude": lon,
            "current": ["soil_moisture_0_to_1cm", "soil_moisture_1_to_3cm", "soil_moisture_3_to_9cm", "soil_moisture_9_to_27cm", "soil_moisture_27_to_81cm"],
            "timezone": "auto"
        }
        
        try:
            resp = requests.get(self.FORECAST_URL, params=params, timeout=20)
            if resp.status_code == 200:
                current = resp.json().get("current", {})
                profile = {
                    "surface_0_1cm": current.get("soil_moisture_0_to_1cm"),
                    "shallow_1_9cm": round(((current.get("soil_moisture_1_to_3cm", 0) + current.get("soil_moisture_3_to_9cm", 0)) / 2), 3),
                    "root_zone_9_27cm": current.get("soil_moisture_9_to_27cm"),
                    "deep_zone_27_81cm": current.get("soil_moisture_27_to_81cm"),
                }
                
                logger.debug(f"Root Zone Moisture: {profile['root_zone_9_27cm']} m3/m3")
                return {"live_moisture_profile": profile}
            else:
                logger.warning(f"Failed to fetch live moisture: HTTP {resp.status_code}")
                return {}
        except Exception as e:
            logger.error(f"Moisture fetch error: {e}")
            return {}

if __name__ == "__main__":
    tool = AgroClimateIntelligenceTool()
    print("\n--- Testing Agronomic Baseline ---")
    baseline = tool.fetch_annual_agronomic_baseline(18.5204, 73.8567)
    print(json.dumps(baseline, indent=2))
    
    print("\n--- Testing Live Soil Moisture ---")
    moisture = tool.fetch_current_soil_moisture_stress(18.5204, 73.8567)
    print(json.dumps(moisture, indent=2))